nbviewer | web service : Render Jupyter Notebooks | Data Visualization library

 by   jupyter Python Version: 1.0.1 License: Non-SPDX

kandi X-RAY | nbviewer Summary

kandi X-RAY | nbviewer Summary

nbviewer is a Python library typically used in Analytics, Data Visualization, Jupyter applications. nbviewer has no vulnerabilities, it has build file available and it has medium support. However nbviewer has 29 bugs and it has a Non-SPDX License. You can download it from GitHub.

Jupyter NBViewer is the web application behind The Jupyter Notebook Viewer, which is graciously hosted by OVHcloud. Run this locally to get most of the features of nbviewer on your own network. If you need help using or installing Jupyter Notebook Viewer, please use the jupyter/help issue tracker. If you would like to propose an enhancement to nbviewer or file a bug report, please open an issue here, in the jupyter/nbviewer project.

            kandi-support Support

              nbviewer has a medium active ecosystem.
              It has 2088 star(s) with 541 fork(s). There are 93 watchers for this library.
              It had no major release in the last 6 months.
              There are 172 open issues and 405 have been closed. On average issues are closed in 96 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of nbviewer is 1.0.1

            kandi-Quality Quality

              nbviewer has 29 bugs (1 blocker, 0 critical, 8 major, 20 minor) and 51 code smells.

            kandi-Security Security

              nbviewer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              nbviewer code analysis shows 0 unresolved vulnerabilities.
              There are 7 security hotspots that need review.

            kandi-License License

              nbviewer has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              nbviewer releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              nbviewer saves you 3120 person hours of effort in developing the same functionality from scratch.
              It has 6717 lines of code, 374 functions and 82 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed nbviewer and discovered the below as its top functions. This is intended to give you an instant insight into nbviewer implemented functionality, and help decide if they suit your requirements.
            • Return a dict of the cmdclass to use
            • Extract version information from VCS
            • Get the project root directory
            • Build a ConfigParser object from a root
            • Get a file from a repository
            • Join two URLs together
            • Generate breadcrumbs for given path
            • Cache content and finish it
            • Load default handlers
            • Create Jinja2 environment
            • List repositories
            • Index a notebook
            • Get a value from the cache
            • Store data in a file
            • Finish rendering
            • Log GitHub rate limit
            • Create the versioneer config file
            • Extract the version information
            • Decorator to cache a method
            • Return default uri handlers
            • Returns a list of uri - rewritten uri_rewrites
            • Prepare the authentication
            • Get a gist
            • Scans the setup py file and checks if the versioneer is missing
            • Render an error response
            • Get a list of gist entries
            Get all kandi verified functions for this library.

            nbviewer Key Features

            No Key Features are available at this moment for nbviewer.

            nbviewer Examples and Code Snippets

            Jupyter Notebookdot img1Lines of Code : 22dot img1License : Permissive (MIT)
            copy iconCopy
            Train a new network on a data set with train.py
                Basic Usage : python train.py data_directory
                Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains
                    Set direcotry to save ch  
            ptplot,Getting Started
            Jupyter Notebookdot img2Lines of Code : 11dot img2License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            import pandas as pd
            from bokeh.plotting import show
            from ptplot import PTPlot
            from ptplot.nfl import Field
            from ptplot.plot import Positions
            data = pd.read_csv("YOUR PLAYER TRACKING DATA")
            plot = PTPlot(data) + Field() + Positions("X_COORDINATE_CO  
            whatsappalytics,Example Usage
            Jupyter Notebookdot img3Lines of Code : 11dot img3no licencesLicense : No License
            copy iconCopy
            path = "/path/to/your/whatsappbackupt.txt"
            languages = ['german', 'english'] # needed to exclude stopwords in wordcloud
            exclude = ['Media omitted', 'Audio omitted'] # whatever messages you want to exclude
            wa = Whatsapp_Analytics(path, languages=lang  

            Community Discussions


            How do I take gradients of MultibodyPlant computations w.r.t. mass, center-of-mass, inertia, etc.?
            Asked 2021-Jun-09 at 12:41

            I see the current chapter of Underactuated: System Identification and the corresponding notebook, and it currently does it through symbolics.

            I'd like to try out stuff like system identification using forward-mode automatic differentiation ("autodiff" via AutoDiffXd, etc.), just to check things like scalability, get a better feel for symbolics and autodiff options in Drake, etc.

            As a first steps towards system identification with autodiff, how do I take gradients of MultibodyPlant quantities (e.g. generalized forces, forward dynamics, etc.) with respect to inertial parameters (say mass)?



            Answered 2021-Jun-09 at 12:41

            Drake's formulation of MultibodyPlant, in conjunction with the Drake Systems framework, can allow you to take derivatives (via autodiff) with respect to inertial parameters by using the parameter accessors of RigidBody on the given plant's Context.

            Please see the following tutorial:

            Source https://stackoverflow.com/questions/67754125


            Preview Jupyter Notebooks in Ubuntu
            Asked 2021-Jun-08 at 12:44

            Peeking at Jupyter notebooks is a long process, need to cd to the file location and need to run jupyter notebook to start the server and get things done, but I don't need to edit or work with it. Is there any other possible way to just take a look at notebooks quickly?

            Looking for the Linux equivalent of this tuxu/nbviewer

            PS: Already know about a Firefox extension but it is broken now.



            Answered 2021-Jun-07 at 14:56

            In the Linux terminal, you can use the jupyter-lab command to start up JupyterLab to access the Notebook.

            If you don't have it installed already, type in the command pip install jupyterlab or pip3 install jupyter .

            Source https://stackoverflow.com/questions/67873757


            Plot graph with vertical labels on the x-axis Matplotlib
            Asked 2021-Jun-04 at 06:30

            To continue my research on how to plot a xml file and continue checking my code, I first applied a division to signal.attrib ["Value"], since it shows some string values ​​and what I'm interested in is the numeric values.

            And as you can see below, I relied on the documentation for Pandas and SQL Compare.



            Answered 2021-Jun-03 at 15:25

            Yes you can, with xticks().

            Source https://stackoverflow.com/questions/67823936


            Interaction between panel and holoviews
            Asked 2021-Mar-04 at 07:44

            I am working on a little widget with holoviews and panel - it consists of reading a pandas.dataFrame and display a curve for each column. The interaction I need is to be able to add/remove columns from the plot. In my real use case, there are too many columns so I can’t take advantage of the interactive legend already provided by bokeh+holoviews.

            I made a little example that ‘’’ kind of works ‘’’ but I am probably doing it wrong, as I am reloading the data for the plot every time there is an interaction with the panel.widgets.MultiChoice (which is obviously wrong)



            Answered 2021-Mar-03 at 11:24

            I think you just need to do your data loading first and not overwrite the dataframe, like:

            Source https://stackoverflow.com/questions/66445536


            Why pyLDAvis graph does not display topic keywords on the bar chart?
            Asked 2021-Feb-20 at 19:51

            I am trying to visualise results of an LDA Model using PyLDAvis. I have managed to get the graphs to display in jupyter notebook, however, the labels of the keywords describing the topics (on the bar chart) are missing.

            Below is an example of the code using dummy data.



            Answered 2021-Feb-12 at 20:10

            !pip install pyLDAvis==2.1.2

            I got this problem as well and this helped. Older version of pyLDAvis does not work properly with Jupyter or Colab.

            Source https://stackoverflow.com/questions/66123774


            FiPy 2D Navier Stokes implementation: problem with derivatives in one direction
            Asked 2021-Feb-19 at 01:47

            I am trying to implement the example from https://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/14_Step_11.ipynb, but I am facing some problems. I think that the main problem is that I am having some issues with the boundary conditions, as well as defining the terms in the equations.

            The PDEs are:

            And the initial & boundary conditions are:

            I understand that my variables are the two components of the velocity vector (u, v), and the pressure (p). Following the example and using FiPy, I code the PDEs as follows:



            Answered 2021-Feb-19 at 01:47

            Here is what I think is an improved version. At least the result looks more reasonable. The major changes are as follows.

            • Using the trick outlined in the linked CFD notebook with the divergence over the time step in the pressure equation.
            • Changing the vector velocity, v, to be a face variable so that we can use .divergence directly. Certainly cleans things up, but is a different discretization. I don't know which is more valid.
            • Fixing the boundary conditions. I'm not sure p.grad.dot[].constrain was doing anything sensible. Anyway, they aren't needed for a zero gradient as that's the default.
            • Not solving all the equations in one matrix. That's best to do once you are confident of solving separately correctly and you have a benchmark to check against.
            • The velocity vector variable was being recreated at each step which means that is was having no impact on the equations. v is now explicitly updated in the loop.
            • Not using a ConvectionTerm when adding the pressure gradient to the momentum equation. The ConvectionTerm is doing weird weighting and isn't exactly a straightforward difference. In the long run it might be good to use, but not whilst debugging.

            Here is the code.

            Source https://stackoverflow.com/questions/66243908


            How to Share a Jupyter Notebook with a CSV Input
            Asked 2020-Oct-21 at 11:34

            I am building a heat map for my company from a generated csv. After a lot of reading I built it using jupyter notebooks (online) using the gmaps API. I've never done anything like this before and jupyter/python/gmaps made it easy to accommodate my requirements (allows for zooming, hovering over a point displays data, easy data cleaning).

            Now I've reached the point where I need to share what I have built (with non-programmers). Right now the user can hit a button and a csv generates, I then take that csv and manually place it in my jupyter notebook folder, my code reads it in via pandas and generates the heatmap. I've seen a lot of resources pointing to github, nbviewer, Colaboratory and others but can't seem to find anything that allows user input. Ideally users would be able to click the button and a heatmap would appear (online or not). Security is also an issue so I am not able to make this project public.

            Is there a way to make jupyter notebooks (with gmaps) into an executable that allows input? Or another way to securely share my project that automates the csv upload? Am I looking at this from completely the wrong angle and need to change my technology?

            Thank you very much in advanced! I am new to this kind of project and appreciate any help :)

            More Info: Windows 10, General environment: visual studio 2019 C#, Current project: python 3



            Answered 2020-Sep-06 at 16:09

            I decided I was thinking about jupyter notebooks in the wrong way and that that technology is more for research/data science. I decided to just make a website that launches when the user clicks the button and have the data auto upload. I changed my technology to google maps javascript api and this seems to do everything I need it to do. I changed my data from csv to json but otherwise I think this is the right answer for me.

            Source https://stackoverflow.com/questions/63730494


            How to correct coordinate metadata attributes like the time calendar in an Xarray dataset?
            Asked 2020-Sep-08 at 09:21

            The NetCDF file I'm reading into xarray unfortunately has the calendar attribute on the time coordinate specified as gregorian_proleptic instead of the CF-standard proleptic_gregorian.

            How can I fix this?

            I tried just changing the attribute, but xarray must have that metadata already stashed somewhere else that I need to modify because when I try to use decode_cf, it still thinks the calendar is gregorian_proleptic. Here's what I tried:



            Answered 2020-Sep-08 at 09:21

            In the dataset in question there is another non-coordinate variable (dstart) which also got the erroneous gregorian_proleptic calendar attribute.

            If this is set correctly:

            Source https://stackoverflow.com/questions/63725011


            How to use another pretrained BERT model with the ktrain text classifier?
            Asked 2020-Sep-03 at 22:09

            How can we use a different pretrained model for the text classifier in the ktrain library? When using:

            model = text.text_classifier('bert', (x_train, y_train) , preproc=preproc)

            This uses the multilangual pretrained model

            However, I want to try out a monolingual model as well. Namely the Dutch one: ''wietsedv/bert-base-dutch-cased', which is also used in other k-train implementations, for example.

            However, when trying to use this command in the text classifier it does not work:



            Answered 2020-Sep-03 at 22:09

            There are two text classification APIs in ktrain. The first is the text_classifier API which can be used for a select number of both transformers and non-transformers models. The second is the Transformer API which can be used with any transformers model including the one you listed.

            The latter is explained in detail in this tutorial notebook and this medium article.

            For instance, you can replace MODEL_NAME with any model you want in the example below:


            Source https://stackoverflow.com/questions/63729057


            Python: Need to overlap the actual data in a boxplot
            Asked 2020-Aug-20 at 14:19

            I'm using this code to plot my data in boxplot:



            Answered 2020-Aug-20 at 14:19

            The current version of plt.boxplot() allows plotting most of these elements standard. Means will be drawn if showmeans is set to True. Its properties can be controlled via the meanprops dictionary. When setting patch_artist=True, instead of just the outline, a filled box will be drawn, boxprops controls how they look.

            To draw the scatter plot on top, just call ax1.scatter. The x-positions can be jittered randomly via i + np.random.uniform(-0.4, 0.4). To force them on top of boxplot, their z-order can be changed.

            As the fliers are also part of the scatter data, it probably makes sense to leave them out (showfliers=False).

            To create a legend, you can collect handles to all desired elements and pass them to ax1.legend(). Note that your boxplots already get labels in the x-axis, so having them also in the legend might be a bit superfluous.

            Source https://stackoverflow.com/questions/63501523

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install nbviewer

            The Notebook Viewer requires several binary packages to be installed on your system. The primary ones are libmemcached-dev libcurl4-openssl-dev pandoc libevent-dev libgnutls28-dev. Package names may differ on your system, see salt-states for more details. If they are installed, you can install the required Python packages via pip. Static assets are maintained with bower and less (which require having npm installed), and the invoke python module. This will download the relevant assets into nbviewer/static/components and create the built assets in nbviewer/static/build. Pass -d or --debug to invoke less to create a CSS sourcemap, useful for debugging. This will automatically relaunch the server if a change is detected on a python file, and not cache any results. You can then just do the modifications you like to the source code and/or the templates then refresh the pages.


            If you would like to contribute to the project, please read the CONTRIBUTING.md. The CONTRIBUTING.md file explains how to set up a development installation and how to run the test suite.
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